Electronic Medical Record Summary and Presentation

ABSTRACT

Methods, devices, and systems (for outputting a case summary) receive an electronic medical record (EMR) (and generally electronic records) for the medical patient, extract medical data from the EMR, provide a list of medical problems relevant to the EMR, identifying relations between the medical problems and the medical data using a question-answering (QA) system, and output the clinical summary for the EMR. The clinical summary comprises the list of medical problems, the medical data, and the relations.

BACKGROUND

The present disclosure relates to question-answering technology, andmore specifically, to systems and methods for creating patient casesummaries from electronic medical records (EMRs) usingquestion-answering technology.

In the medical domain, a patient's medical conditions and treatmenthistory are often stored in collection referred to as an electronicmedical record (EMR). An EMR may include large volumes of plain textclinician notes, procedures performed along with results, structureddata such as medications, and images such as CT scan images. An EMR maycontain both structured and unstructured data resulting from a varietyof medical care encounters of a patient with medical care providers.Examples of information that may be contained in an EMR includedemographic information, family and social history, reports from careproviders, procedures undergone, medications prescribed, diagnostic testresults, vital signs, and administrative reports. Therefore, EMRs tendto be large; for example, an EMR of a single patient may containhundreds of megabytes of structured and unstructured content. Anaggregated clinical summary of patient information in the EMR is of highvalue in patient care if it shows the most important points relevant topatient care.

SUMMARY

Exemplary methods and computer program products herein (for outputting aclinical summary for a medical patient) receive an electronic medicalrecord (EMR) for the medical patient, and extract medical data from theEMR. A list of medical problems relevant to the EMR is provided. Thesemethods and computer program products identify relations between themedical problems and the medical data using a question-answering (QA)system and output the clinical summary for the EMR. The clinical summarycomprises the list of medical problems, the medical data, and therelations.

Systems herein output a clinical summary for a medical patient. Thesesystems comprise modules having program instructions embodied therewith.A receiving module of the modules receives an electronic medical record(EMR) for the medical patient and a list of medical problems. Anextracting module of the modules extracts medical data from the EMR. Arelation identification module of the modules identifies relationsbetween the medical problems and the other medical data using aquestion-answering (QA) system. An outputting module of the modules,outputs the clinical summary for the EMR. The clinical summary comprisesthe list of medical problems, the medical data, and the relations.

BRIEF DESCRIPTION OF THE DRAWINGS

The systems and methods herein will be better understood from thefollowing detailed description with reference to the drawings, which arenot necessarily drawn to scale, and in which:

FIG. 1 is a schematic diagram illustrating process flow according tosystems and methods herein;

FIG. 2 is an example of a template for creating an aggregated clinicalsummary page according to systems and methods herein;

FIG. 3 is an example of an aggregated clinical summary page according tosystems and methods herein;

FIG. 4 is an example of a map of specific medications/problems tomultiple general classes according to systems and methods herein;

FIG. 5 is an example of medication clustering according to systems andmethods herein;

FIG. 6 is an exemplary threshold chart according to systems and methodsherein;

FIG. 7 is an illustration of a summary page according to systems andmethods herein;

FIG. 8 is an illustration of a summary page showing a time-based trendgraph according to systems and methods herein;

FIG. 9 is a flow diagram illustrating systems and methods herein;

FIG. 10 is a schematic diagram illustrating modules of systems herein;

FIG. 11 is a schematic diagram of a hardware system according to systemsand methods herein;

FIG. 12 is a schematic diagram of a computing environment according tosystems and methods herein; and

FIG. 13 is a schematic diagram of functional abstract layers accordingto systems and methods herein.

DETAILED DESCRIPTION

It will be readily understood that the systems and methods of thepresent disclosure, as generally described and illustrated in thedrawings herein, may be arranged and designed in a wide variety ofdifferent configurations in addition to the systems and methodsdescribed herein. Thus, the following detailed description of thesystems and methods, as represented in the drawings, is not intended tolimit the scope defined by the appended claims, but is merelyrepresentative of selected systems and methods. The followingdescription is intended only by way of example, and simply illustratescertain concepts of the systems and methods, as disclosed and claimedherein.

The present invention enables creation of a case summary forpresentation of a collection of unstructured case information. Thepresent invention as described herein is directed to a specificembodiment of providing a case summary for a patient's ElectronicMedical Record (EMR). However, the present invention is not limited toproviding a case summary for an EMR, but can be extended to anycollection of unstructured case information. Any collection ofunstructured information that lends itself to being summarized based ona list of relevant entities can benefit from the present invention.

Generally, the present invention uses a list of entities of interestrelated to the case information, creates questions based on the list ofentities of interest, enters the created questions into aquestion-answering (QA) system using an unstructured domain corpus,extracts case information summary entities based the answers to thosequestions, outputs the list of entities of interest along with the caseinformation summary entities, and indicates relationships between theentities of interest and the summary entities. The QA system can be anyQA system capable of producing concise answers to given questions.However, the case summaries will vary in content and/or qualitydepending on the QA system used. A preferred QA system is the IBM®Watson QA system, or instances of the IBM® Watson® system, developed byIBM® and described with greater detail in the IBM Journal of Researchand Development, Volume 56, Number 3/4, May/July 2012, the contents ofwhich are hereby incorporated by reference in its entirety. (Trademarksprovided herein are the property of their respective owners)

The present invention will be described in further detail with referenceto a specific embodiment of the invention as used to provide a casesummary based on a patient's EMR. With the advent of EMR systems,medical data is primarily captured and stored in a digital form.However, in many cases, the information is unstructured and the amountof information related to a single patient can easily range in thehundreds of megabytes. There is a need to present a summary of alongitudinal patient EMR (also referred to more broadly as caseinformation) in order to assist a medical provider in providing medicalcare to the patient. The summary must be succinct, accurate, and showthe most important data items from the EMR relevant to the patient'scare.

Retrieving the most important data items from an EMR is difficult for anumber of reasons. EMRs are typically very large, so maintaining summaryinformation by hand can be extremely difficult. Moreover, simple keywordmatching from a list of problems is insufficient, particularly in themedical field because it does not consider medical semantics.Furthermore, queries prepared in a semantic information retrievallanguage and run against a semantic database are dependent onformulating effective queries, which is not trivial.

Referring now to the specific embodiment of the present invention ofcreating a case summary for a patient's EMR as shown in FIG. 1. Thisembodiment of the present invention works by first obtaining a patient'sEMR 111 from an EMR system 112 and analyzing the EMR in an EMRAnnotation and Problem List Generation Module 113. The contents of theEMR 111 are analyzed using natural language processing techniques alongwith a medical ontology to recognize medical concepts within the EMR111. The contents of the entries in the EMR 111 are annotated toidentify the medical concepts and stored as an annotated EMR 120.

Natural language processing of the EMR 111 may use an ontology such asthe Unified Medical Language System (UMLS). UMLS may be used in medicalcontexts to analyze clinical notes, such as the Clinical Text Analysisand Knowledge Extraction System (CTAKES). Certain text in an EMR maycontain CUI (Concept Unique Identifier) fields. In these cases, a CUIsearch will yield results that exist within that concept. The EMR 111may include structured data and unstructured data comprising, forexample, demographics, family and social history, physician, nurse, andother care provider reports, procedures, medications, diagnostic testresults, vital signs, and administrative reports.

In order to use lab findings and other numeric measurements in themedical domain, the system employs recognition capabilitiesincorporating context. For instance, a statement such as “320 mg/dLblood glucose” can use context to map to “Hyperglycemia.” While in somecases this information may be associated with health records instructured form, in other cases the information is recorded asunstructured text. In one example, the present invention may provide arule-based annotator that identifies measurements and test results asexpressed in text. Based on existing guidelines, measurements areinterpreted to be normal, high, or low, and mapped using general tablesto the corresponding UMLS concept. Normal, high, and low values may alsobe expressed lexically (e.g. “elevated T4”) and the methods can havetrained statistical classifiers. Additionally, the methods may havecollected a set of mapping rules to map to specific concepts in UMLSwhen they exist (e.g., mapping from “blood pressure is elevated” to the“Hypertension” concept).

In the methods herein, the contents of the EMR 111 have been annotatedto identify medical concepts from the semantic features within the EMR111 and syntactic features of the contents. A medical problem list 301may be generated from the EMR 111 using named entity and relationannotators to extract clinical data. The clinical data may includesigns, symptoms, findings, active and past diseases, currentmedications, allergies, demographics, social or family history, and manyothers. The concepts need to be broad enough to capture the descriptiveintent of the clinician and interpret medical semantics. For example,hypertension to a clinician means “high blood pressure.” Relations, forexample, that indicate a specific family member had a particulardisease, or that a symptom is mentioned in negation, need to beaccurately captured from the language parse results. Laboratory testresults need to be interpreted and evaluated for clinical significance.The extraction of this information from the patient's EMR providescontext for generation of the medical problems list 301.

The present invention obtains a medical problems list 301 that can beentered manually by a medical professional, provided from an externalsource, or the list can be generated automatically from the medical datain the EMR 111 using the EMR Annotation and Problem List GenerationModule 113. The preferred methodology for generating the problem listfrom the EMR for the present invention is described in greater detail ina related patent application entitled, “Automated Medical Problem ListGeneration from Electronic Medical Record,” (IBM Docket YOR920140007US1)filed as U.S. patent application Ser. No. 14/300,699 and herebyincorporated by reference in its entirety.

After obtaining the patient's medical problems list 301, the presentinvention generates clinical data relationship questions 139 based uponthe medical problems list 301 using a Questions Generation Module 138.Clinical data relationship questions 139 are generated for each item onthe medical problems list 301. The clinical data relationship questions139 are automatically generated using question templates 141 along withthe entities in the medical problems list 301 and the medical conceptsfrom the annotated EMR 120. The question templates 141 are pre-definedrelation questions for categories of medical data that are commonlyfound in EMRs. Non-limiting examples of clinical data relationshipquestions include (1) “What medication treats [disease]”, (2) “Whatmedical condition is treated by [medications]”, (2) “What clinical testshould be considered for a family history of [family history entry].”

The question templates can include open-ended question formats ormultiple-choice formats. For example, if the problems on a patient'smedical problems list 301 included acute sinusitis, diabetes, andgastroesophageal reflux disease and a number of medications wereidentified from the annotated EMR 120 including glipizide, cataflam andfluticasone, then some non-limiting resulting clinical data relationshipquestions 139 may include: “What medication treats acute sinusitis, (a)glipizide, (b) cataflam, or (c) fluticasone?” and “What medicalcondition is treated by glipizide, (a) acute sinusitis, (b) diabetes, or(c) gastroesophageal reflux disease?” The question templates 141 areautomatically selected and utilized based on the medical concepts in theannotated EMR 120.

After the clinical data relationship questions 139 are generated fromthe Questions Generation Module 138, the clinical data relationshipquestions 139 are entered into a question-answering (QA) system 147. TheQA system uses a corpus of data 148 relevant to the domain to obtainanswers to the clinical data relationship questions 139. The QA systemis preferably a probabilistic QA system such as the IBM Watson QAsystem, or instances of the IBM Watson system, developed by IBM anddescribed with greater detail in the IBM Journal of Research andDevelopment, Volume 56, Number 3/4, May/July 2012.

Each of the clinical data relationship questions 139 is input to the QAsystem 147, which automatically searches the corpus of data 148, whichcan include structured and unstructured data, to retrieve answers to theclinical data relationship questions 139. The QA system 147 should becapable of analyzing natural language questions and generating candidateanswers from unstructured text, however, the specific techniques used toproduce the answers can vary. For example, QA system 147 may includemultiple semantic search techniques, such as string matching, LatentSemantic Analysis (LSA) search, Logical Form Answer Candidate Scorer(LFACS) term matching, and relations-based search.

String matching may include an Indri search query built with the inputquestion or search terms and run against the index or a structured datasearch string match within structured data fields. For example,Structured Term Recognition (STR) may recognize new terms of a specifictype based on the structure of known terms for that type, e.g. “skincancer” is a term and is a type of cancer.

An LSA search may incorporate pair-wise matching of each CUI from eachnote in the EMR, with all CUIs in the input. For example, LSA recognizesstatistical association between two entities such as words, CUIs, orterms, based on their occurrence in the corpus, e.g. Hyperlipidemia andHigh Cholesterol are similar. High CUIs, and Notes with them are thematches. A similar match can be conducted with the structured data CUIs.

In LFACS term matching, each term from each Note may be semanticallymatched with terms from the clinical data relationship questions 139.Those Notes having a number of matches over a predetermined thresholdmay be kept. A similar match can be conducted with the structured data.

A relations-based search may use relations with the structured data toextract relevant passages, e.g. “simvastatin” in a query (matched with aCUI) may lead to CUIs in Notes through a “treats” relation.

Herein a description is provided with reference to the use of the IBMWatson QA architecture in the present invention. The QA system 147 (i.e.IBM Watson QA System) conducts a primary content specific search withina medical corpus for candidate answers and associated medically relevantnotes, snippets, terms, and structured data. Once the search results arereturned, the retrieved results are scored on a variety of measures ofsemantic match, medical relationship strength, and other criteria.

Candidate responses may be scored based on medical knowledge frommedical corpora, including books, web pages, and other text-basedknowledge representations. A score may be determined for the candidateresponses based on the degree of semantic match of the passages to theclinical data relationship questions 139. Another score may bedetermined for the candidate responses based on the strength of medicalrelationship of the passages to the clinical data relationship questions139. Additionally, the scores may be determined by how easily acandidate response may be “coerced” to the desired lexical answer typeof the question. For additional information on this scoring techniqueplease refer to the IBM Journal of Research and Development, Volume 56,Number 3/4, May/July 2012.

Answers are obtained from the QA system 147 based on the highest scoringcandidate responses. After the answers and corresponding scores areobtained, the answers are analyzed in order to identify and recordrelationships between the medical problems and the medical data. Forexample, in FIG. 1, the answers are input into an Answers Analysis andRelations Generation Module 156 to extract relationship data between,for example, problems and medications and/or problems and laboratorytest results, based on the answers provided by the QA system 147. Thedetails of these analytics are described below.

FIG. 2 shows an example of relationship confidence scores 102 ofdifferent problems 104 returned by the QA system 147 for some clinicaldata relationship questions 139. For example, for a problem of“hyperlipidemia” identified from the patient's medical problems list301, and an identified medication of “ephedrine sulfate” from theannotated EMR 120, the QA system 147 may identify a relationship scoreof 0.06. Based on this score, the systems and methods herein determinewhether or not to record the medication as having a relationship to theproblem based on a threshold value. This threshold value can bedependent on the type of relationship, and it can be determined by anymethod, but is preferably determined using machine learning techniques.For example, a machine learning technique can be used to determine whatvalue of the score correctly predicts a valid relationship for a “istreated by” relationship. It's possible that a “is treated by”relationship requires a threshold score of at least 0.5 and thereforeonly “hydrochlorothiazide,” “Lisinopril,” “atorvastatin,” and“amlodipine” are recorded as being related to the problem of“hyperlipidemia.”

FIG. 3 shows an example of a relationship mapping 106 of specificmedications 108 to related problems 110 according to systems and methodsherein. Based on a threshold determination of a valid relationship asdescribed in the paragraph above, the medical concepts in the medicalproblems list 301 are assigned a yes or no score (1 or 0) as beingrelated to medical data from the EMR 111, such as medication, familyhistory, lab results, etc. These relationships are stored and can beindicated in the EMR summary according to an embodiment of the presentinvention.

FIG. 4 shows how medical concepts can be grouped together 114 and 116based on relationship similarities discovered from the QA system 147.For example, as described above, each medical concept may be recorded ashaving a valid relationship to problems as indicated by a yes or noscore (1 or 0). If the number of relations is sufficiently large, thenthe medical concepts (in this case—medications) can be clustered basedon their cosine similarity (i.e. correlation) using problem-relationvectors. Then medications in the same cluster, or even a “nearby”cluster, can be grouped together for filtering and organizing theinformation to be output. In other words, a vector of taxonomicallyreachable target classes is developed for each instance.

The Answers Analysis and Relations Generation Module 156 provides inputto the EMR Summary Generation Module 159 that includes a summarizationtemplate 165, which may be used to determine which answers and relationsto provide in the aggregated summary. FIG. 5 is an example of a template118 for creating an aggregated clinical summary page according tosystems and methods herein. After the answers are analyzed, therelations identified, and the entities grouped, the summarizationtemplate 165 may be used to determine which answers and relations toprovide in the aggregated summary. The summarization template 165 may becustomized by specifying the kinds of information to show. As shown inFIG. 5, the summarization template 165 may be built into software codeor may be fixed.

An example of a template for use in a medical domain may provide for therendering of the following types of information extracted from the EMR:

(1) Encounters and episodes timelines(2) Generated list of problems(3) Disease trajectories(4) Active and inactive medications(5) Family and social history(6) Known allergies(7) Important procedures(8) Selected lab results(9) Medical relationships among the clinical data.

FIG. 6 shows an example of an aggregated clinical summary (i.e. EMRsummary) according to aspects of the present invention that will beshown on screen of a user's device 170 (e.g. laptop, PC, or tablet). Thesummary page may be arranged in discrete sections that show the medicalproblems list 301 as well as a clinical encounters timeline 302,laboratory test values 303, medications 304, social history 305, andallergies 306. In this example, the various clinical encounters 302 maybe broken down into primary care, specialty, Inpatient, nursing, andother encounters. The aggregated clinical summary provides aproblems-oriented summary of a longitudinal patient record that assistsa physician by enabling rapid understanding of a patient's health andcare management history. The aggregated clinical summary, as shown inFIG. 6, also provides indications of relationships between problems andthe data in the other categories by highlighting, using colors, usingsymbols, or other appropriate methods.

As shown in FIGS. 7 and 8, the EMR summary also provides for interactionby a user physician to drill down into more details (309) with respectto summarized information from the EMR. The ability to drill down tomore details directly from the summary view enables rapid access tospecific relevant medical information. First, as shown in FIG. 7, thedrill down feature 309 provides access to the original clinical notes inthe EMR. When the user physician selects a clinical encounter from thetimeline in the summary view the clinical notes from that encounter areprovided. Alternatively, as shown in FIG. 7, the user physician can beshown snippets of relevant information from the EMR with links to thefull details. Second, as shown in FIG. 8, the clinical summary maypresent timelines of lab results 407 and medications enabling a view ofcare progression over time. In the example shown in FIG. 8, any of thelaboratory test values 303 or medications 304 can be selected in orderto show time-based trends of the lab values or medication amounts forthe patient as well as preferred low value 414 and preferred high value421.

FIG. 9 is a flow diagram illustrating the processing flow of anexemplary method according to systems and methods herein. In step 510,an EMR 111 is received and analyzed by a processor to generate a medicalproblem list 301 and an annotated EMR 120 including medical conceptsidentified within the EMR 111. In step 520, questions 139 are generatedusing question templates 141 along with the medical problem list 301 andthe medical data from the annotated EMR 120. The questions 139 relatethe data in the annotated EMR 120 to entities in the medical problemlist 301, or data in the annotated EMR 120 to medical concepts ingeneral. In step 530, the questions 139 are input into a questionanswering (QA) system 147, for example, IBM Watson. In step 540, the QAsystem 147 will generate answers to the questions by processing througha corpus of data 148, which may include unstructured information, forexample, unstructured text. The corpus of data 148 may be maintained inat least one database separate from the electronic record. In step 550,the answers are received from the QA system 147 along with correspondingprobabilistic scores indicating a degree of confidence/probability thatthe answers correctly answer the question. In step 560, the answers areanalyzed to identify valid relations between the medical problems andmedical data, or among medical concepts. The valid relations are basedon the probabilistic scores and threshold values determined to identifya valid relation. In step 570, a clinical summary of the EMR 111 isoutput on the output device 170 for use by a medical provider. Theclinical summary provides the medical data identified from the EMR 111based on the relations to the medical problem list 301 and other medicaldata. The information output is based on a summarization template 165,the answers obtained and the corresponding scores. The informationoutput includes representations of relations between the medicalproblems and the other medial data.

In a clinical setting, for example, the present invention can be used todevelop a support tool that uses the context of an input case, a richset of observations about a patient's medical condition, and generates asummary of medically relevant information indicating relations among theinformation with associated confidences based on searching and analyzingevidence from large volumes of content. Physicians and other careproviders may more easily evaluate these vast amount of informationalong many different dimensions of evidence that can be extracted from apatient's EMR and other related content sources. For medicine, thedimensions of evidence may include symptoms, findings, patient history,social/family history, demographics, current medications, and manyothers. According to systems and methods herein, the output summary mayinclude links back to the original evidence used to produce the summaryand the confidence scores and supports the adoption of evidence-basedmedicine.

The aggregated clinical summary is succinct and accurate, and shows themost important points relevant to the patient's care. As describedabove, the summary output also enables the ability to drill down to theprimary evidence (e.g. raw data) in order to see timelines,trajectories, and medically relevant relationships among the aspects ofthe patient care shown on the screen of a user's device 170 (e.g.laptop, PC, or tablet).

FIG. 10 illustrates exemplary systems herein that output a clinicalsummary for a medical patient. These systems comprise modules havingprogram instructions embodied therewith. The modules are not atransitory signal per se, and the program instructions are executable bya computer, to perform a method. A receiving module 610 receives anelectronic medical record (EMR) for the medical patient. An extractingmodule 620 extracts a list of medical problems and other medical datafrom the EMR. A relation identification module 630 identifies relationsbetween the medical problems and other medical data using aquestion-answering (QA) system. An outputting module 640, outputs theclinical summary for the EMR. The clinical summary comprises the list ofmedical problems, the medical data, and the relations.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

In the on-demand self-service: a cloud consumer can unilaterallyprovision computing capabilities, such as server time and networkstorage, as needed automatically without requiring human interactionwith the service's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based email). Theconsumer does not manage or control the underlying cloud infrastructureincluding network, servers, operating systems, storage, or evenindividual application capabilities, with the possible exception oflimited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting for loadbalancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 10, a schematic of an example of a cloud computingnode is shown. Cloud computing node 10 is only one example of a suitablecloud computing node and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the inventiondescribed herein. Regardless, cloud computing node 10 is capable ofbeing implemented and/or performing any of the functionality set forthhereinabove.

In cloud computing node 10 there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, handheld or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 10, computer system/server 12 in cloud computing node10 is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 12 may include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnect (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 11, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 11 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 12, a set of functional abstraction layersprovided by cloud computing environment 50 (FIG. 11) is shown. It shouldbe understood in advance that the components, layers, and functionsshown in FIG. 12 are intended to be illustrative only and embodiments ofthe invention are not limited thereto. As depicted, the following layersand corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include mainframes, in oneexample IBM® zSeries® systems; RISC (Reduced Instruction Set Computer)architecture based servers, in one example IBM pSeries® systems; IBMxSeries® systems; IBM BladeCenter® systems; storage devices; networksand networking components. Examples of software components includenetwork application server software, in one example IBM WebSphere®application server software; and database software, in one example IBMDB2® database software. (IBM, zSeries, pSeries, xSeries, BladeCenter,WebSphere, and DB2 are trademarks of International Business MachinesCorporation registered in many jurisdictions worldwide).

Virtualization layer 62 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers;virtual storage; virtual networks, including virtual private networks;virtual applications and operating systems; and virtual clients.

In one example, management layer 64 may provide the functions describedbelow. Resource provisioning provides dynamic procurement of computingresources and other resources that are utilized to perform tasks withinthe cloud computing environment. Metering and Pricing provide costtracking as resources are utilized within the cloud computingenvironment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal provides access to the cloud computing environment forconsumers and system administrators. Service level management providescloud computing resource allocation and management such that requiredservice levels are met. Service Level Agreement (SLA) planning andfulfillment provide pre-arrangement for, and procurement of, cloudcomputing resources for which a future requirement is anticipated inaccordance with an SLA.

Workloads layer 66 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation; software development and lifecycle management; virtualclassroom education delivery; data analytics processing; transactionprocessing; and EMR summary generation and presentation according to thepresent invention.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A computer-implemented method of outputting asummary for a user, said method comprising: receiving an electronicrecord for said user; extracting data from said electronic record;providing a list of problems relevant to said electronic record;identifying relations between said problems and said data using aquestion-answering (QA) system; and outputting said summary for saidelectronic record, wherein said summary comprises said list of problems,said data, and said relations.
 2. The method according to claim 1,wherein said summary further comprises sections containing conceptsorganized into categories.
 3. The method according to claim 2, whereinsaid sections comprise laboratory test values, medications, and atimeline of clinical encounters.
 4. The method according to claim 3,further comprising: receiving user interaction with a clinical encounteroutput in said timeline of clinical encounters; and outputtinginformation from said electronic record relevant to said clinicalencounter including content from a clinical note relevant to saidclinical encounter.
 5. The method according to claim 3, furthercomprising: receiving user interaction with a laboratory test value or amedication output in said clinical summary; and outputting a timeline ofvalues corresponding to said laboratory test values or amounts ofmedication.
 6. The method according to claim 1, wherein said step ofidentifying relations comprises: generating a question using a questiontemplate based on a medical problem and said data extracted from saidelectronic record; inputting said question into said QA system;obtaining an answer to said question, wherein said answer has acorresponding confidence score; and identifying whether a relationexists between said medical problem and said medical data based on saidconfidence score.
 7. The method according to claim 1, wherein said QAsystem comprises a probabilistic system that analyzes unstructuredinformation and provides answers with confidence scores.
 8. A system foroutputting a summary for a user, said system comprising modulescomprising: a receiving module of said modules receiving an electronicrecord for said user and a list of problems; an extracting module ofsaid modules extracting data from said electronic; a relationidentification module of said modules identifying relations between saidmedical problems and said other medical data using a question-answering(QA) system; and an outputting module of said modules, outputting saidclinical summary for said electronic record, wherein said clinicalsummary comprises said list of medical problems, said medical data, andsaid relations.
 9. The system according to claim 8, wherein said summaryfurther comprises sections containing concepts organized intocategories.
 10. The system according to claim 9, wherein said sectionscomprise laboratory test values, medications, and a timeline of clinicalencounters.
 11. The system according to claim 10, said receiving modulereceiving user interaction with a clinical encounter output in saidtimeline of clinical encounters, and said outputting module outputtinginformation from said electronic record relevant to said clinicalencounter including content from a clinical note relevant to saidclinical encounter.
 12. The system according to claim 10, said receivingmodule receiving user interaction with a laboratory test value or amedication output in said clinical summary; and said outputting moduleoutputting a timeline of values corresponding to said laboratory testvalues or amounts of medication.
 13. The system according to claim 8,said relation identification module: generating a question using aquestion template based on a medical problem and other medical dataextracted from said electronic record; inputting said question into saidQA system; obtaining an answer to said question, wherein said answer hasa corresponding confidence score; and identifying whether a relationexists between said medical problem and said other medical data based onsaid confidence score.
 14. The system according to claim 8, wherein saidQA system comprises a probabilistic system that analyzes unstructuredinformation and provides answers with confidence scores.
 15. A computerprogram product, said computer program product comprising a computerreadable storage medium having program instructions embodied therewith,wherein the computer readable storage medium is not a transitory signalper se, the program instructions being executable by a computer, toperform a method of outputting a summary for a user comprising:automatically, by said computer, receiving an electronic record for saidmedical patient; automatically, by said computer, extracting data fromsaid electronic record; automatically, by said computer, providing alist of medical problems relevant to said electronic record;automatically, by said computer, identifying relations between saidmedical problems and said other medical data using a question-answering(QA) system; and automatically, by said computer, outputting saidclinical summary for said electronic record, wherein said clinicalsummary comprises said list of medical problems, said medical data, andsaid relations.
 16. The computer program product according to claim 15,wherein said clinical summary further comprises sections containingmedical concepts organized into categories.
 17. The computer programproduct according to claim 16, wherein said sections comprise laboratorytest values, medications, and a timeline of clinical encounters.
 18. Thecomputer program product according to claim 17, further comprising:receiving user interaction with a clinical encounter output in saidtimeline of clinical encounters; and outputting information from saidelectronic record relevant to said clinical encounter including contentfrom a clinical note relevant to said clinical encounter.
 19. Thecomputer program product according to claim 17, further comprising:receiving user interaction with a laboratory test value or a medicationoutput in said clinical summary; and outputting a timeline of valuescorresponding to said laboratory test values or amounts of medication.20. The computer program product according to claim 15, wherein saidstep of identifying relations comprises: generating a question using aquestion template based on a medical problem and other medical dataextracted from said electronic record; inputting said question into saidQA system; obtaining an answer to said question, wherein said answer hasa corresponding confidence score; and identifying whether a relationexists between said medical problem and said other medical data based onsaid confidence score.